# -*- coding: utf-8 -*-
# File  : 03-transfer_learning.py
# Author: Pengwenyu
# Date  : 2019/8/23
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms,models
import time
import matplotlib.pyplot as plt
from fastai import *
from torch.autograd import Variable
from fastai.vision import *

cls = ['Top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle_boot']


class FocalLoss(nn.Module):
    r"""
        This criterion is a implemenation of Focal Loss, which is proposed in
        Focal Loss for Dense Object Detection.
            Loss(x, class) = - \alpha (1-softmax(x)[class])^gamma \log(softmax(x)[class])
        The losses are averaged across observations for each minibatch.
        Args:
            alpha(1D Tensor, Variable) : the scalar factor for this criterion
            gamma(float, double) : gamma > 0; reduces the relative loss for well-classiﬁed examples (p > .5),
                                   putting more focus on hard, misclassiﬁed examples
            size_average(bool): By default, the losses are averaged over observations for each minibatch.
                                However, if the field size_average is set to False, the losses are
                                instead summed for each minibatch.
    """

    def __init__(self, class_num, alpha=None, gamma=2, size_average=True):
        super(FocalLoss, self).__init__()
        if alpha is None:
            self.alpha = Variable(torch.ones(class_num, 1))
        else:
            if isinstance(alpha, Variable):
                self.alpha = alpha
            else:
                self.alpha = Variable(alpha)
        self.gamma = gamma
        self.class_num = class_num
        self.size_average = size_average

    def forward(self, inputs, targets):
        N = inputs.size(0)
        C = inputs.size(1)
        P = F.softmax(inputs)

        class_mask = inputs.data.new(N, C).fill_(0)
        class_mask = Variable(class_mask)
        ids = targets.view(-1, 1)
        class_mask.scatter_(1, ids.data, 1.)
        # print(class_mask)

        if inputs.is_cuda and not self.alpha.is_cuda:
            self.alpha = self.alpha.cuda()
        alpha = self.alpha[ids.data.view(-1)]

        probs = (P * class_mask).sum(1).view(-1, 1)

        log_p = probs.log()
        # print('probs size= {}'.format(probs.size()))
        # print(probs)

        batch_loss = -alpha * (torch.pow((1 - probs), self.gamma)) * log_p
        # print('-----bacth_loss------')
        # print(batch_loss)

        if self.size_average:
            loss = batch_loss.mean()
        else:
            loss = batch_loss.sum()
        return loss


batch_size = 64
focal_loss = FocalLoss(class_num=10)
imagelist = ImageList.from_folder("/home/ubuntu/Python-projects/Pytorch_test/fashion_mnist_data/FashionMNIST/Image_data/train")
tfms = get_transforms(do_flip=False)
data = (imagelist
        .split_by_rand_pct(0.2)
        .label_from_folder()
        # .add_test_folder('test')
        .transform(tfms, size=32)
        .databunch()
        .normalize(imagenet_stats))
learn = cnn_learner(data, models.resnet18, metrics=accuracy)
# learn = cnn_learner(data, models.resnet18, metrics=accuracy).load("models/model")

learn.lr_find()

learn.loss_func = focal_loss
learn.fit_one_cycle(256, max_lr=1e-2,wd=0.0001,pct_start=0.1)
learn.save("models/model")
learn.export("models/model.pkl")
# learner = load_learner(mnist, test=ImageList.from_folder(test_dataloader))
# preds, y = learner.get_preds(ds_type=DatasetType.Test)  # 指定数据集类型为测试集
#
callback_fns=ShowGraph
plt.show()